Ai Solution Architect

Purpose 

The AI Solution Architect GSS leads high-priority AI initiatives leveraging AWS Bedrock LangGraph and advanced agentic AI technologies to solve complex business challenges for data scientists AI agents and business users. Acting as the bridge between functional domain experts and technical engineering teams this role translates business needs into robust scalable and well-governed AI-powered solutions.

This position is accountable for the end-to-end design deployment and user experience of AI and analytics products supporting GSS  (IT Support Finance HR Indirect Procurement Legal Facility Management etc.). The AI Solution Architect owns the full AI solution landscape delivered by IT&D teams including architectural design proof-of-concept implementation AI model integration and management of development testing and production environments.

As a key enabler in the GSS AI journey the AI Solution Architect ensures AI solutions are interoperable ethically governed performant and built on the right platforms. The role works closely with the Central Data & Analytics Architecture team and AI Center of Excellence to align functional and enterprise-wide needs ensure the right AI technologies and frameworks are in place and embed Corporate Functions within the organizations AI framework.

Accountabilities

Solution Design for Corporate Functions:

• Design and implement AWS Bedrock-centered AI architectures and agentic patterns to meet the needs of Finance HR Procurement Legal and other corporate functions.
• Ensure AI solutions meet requirements for intelligent automation conversational AI decision support and predictive analytics while aligning with enterprise AI governance security and responsible AI standards.

AI Agent & Solution Architecture:

• Architect agentic AI solutions using LangGraph and multi-agent frameworks defining agent orchestration tool integration memory management and human-in-the-loop patterns for corporate function use cases.
• Translate business requirements from corporate stakeholders into scalable maintainable AI solution designs that leverage foundation models RAG (Retrieval-Augmented Generation) and agent-based workflows.

AI Model Integration & Platform Build:

• Lead the design of AI solution architectures integrating AWS Bedrock foundation models (Claude Titan etc.) with enterprise data sources vector databases and knowledge bases.
• Guide AI Engineers and Data Engineers in implementing LangGraph-based agent workflows prompt engineering strategies and model evaluation frameworks.

Leadership & Collaboration:

• Lead and mentor AI Engineers Data Engineers SMEs and external consultants working on corporate function AI initiatives.
• Work closely with business analysts product owners AI governance teams and legal/compliance to ensure solutions meet functional ethical and regulatory requirements.

Integration & Orchestration:

• Architect agentic integration solutions using LangGraph for multi-step reasoning tool calling and external system integration (SAP Workday ServiceNow etc.).
• Collaborate with platform teams to implement CI/CD pipelines for AI agents including automated testing model versioning and deployment automation on AWS.

AI Modernization & Transformation:

• Contribute to AI transformation strategies migrating legacy automation and analytics solutions to modern agentic AI architectures powered by Bedrock and LangGraph.
• Define patterns for responsible AI deployment including monitoring explainability bias detection and continuous improvement of AI agents in production.

Professional Experience:

• 15 years of overall professional experience including:
  • 10 years in architecting enterprise-scale AI solutions.
• 7 years enterprise software development solution engineering or AI/ML consulting
• 3 years production experience with Large Language Models and generative AI
• 4 years hands-on experience with AWS SageMaker (training deployment pipelines feature store)
• 2 years hands-on experience with Langgraph LangChain Bedrock or Agentic AI frameworks
• Proven track record delivering complex AI projects from conception to production

Technical Expertise:

AI Platforms & Cloud:

• Deep expertise in AWS Bedrock (Claude Titan Jurassic models) and AWS AI/ML services (SageMaker Kendra OpenSearch Textract Comprehend).
• Proficient with AWS infrastructure for AI workloads: Lambda ECS/EKS S3 DynamoDB RDS API Gateway EventBridge Step Functions.
• Experience with vector databases (Pinecone Weaviate pgvector OpenSearch Vector Engine) for RAG implementations.

Agentic AI Frameworks & LLM Orchestration:

• Expert in LangGraph for building stateful multi-agent systems with cyclic workflows human-in-the-loop patterns and complex reasoning chains.
• Proficient in LangChain ecosystem (agents tools chains memory callbacks) and prompt engineering techniques.
• Experience with agent frameworks (Langgraph CrewAI Semantic Kernel) and multi-agent orchestration patterns.

Programming & AI Development:

• Proficient in Python for AI solution development including libraries: boto3 langchain langgraph pydantic FastAPI streamlit.
• Experience with prompt engineering few-shot learning chain-of-thought reasoning and model evaluation frameworks.
• Skilled in building conversational AI interfaces and integrating with collaboration platforms (Slack Teams ServiceNow).

Integration & Tooling:

• Skilled in API integration for AI agents (REST GraphQL webhooks) and tool-calling patterns for external system access.
• Experience with CI/CD for AI (GitHub Actions AWS CodePipeline) including automated testing model versioning and deployment strategies.
• Proficient with collaboration platforms (Jira Confluence Bitbucket) and AI observability tools (LangSmith Weights & Biases MLflow).

AI Architecture Best Practices:

• Ability to design solutions aligned with AWS Well-Architected Framework for AI/ML and Responsible AI principles across security governance explainability bias mitigation and cost optimization.
• Expertise in RAG architecture patterns: chunking strategies embedding models retrieval optimization and context management.
• Knowledge of AI safety and guardrails: content filtering PII detection hallucination mitigation and model monitoring.

Enterprise Systems & Knowledge Integration:

• Experience integrating AI agents with enterprise systems (SAP ECC/S4 Ariba Workday Salesforce ServiceNow) via APIs and connectors.
• Skilled in knowledge base construction from structured and unstructured corporate data sources (SharePoint Confluence databases PDFs).
• Understanding of enterprise authentication (SSO OAuth SAML) and secure API access patterns for AI agents.

Multi-Cloud & AI Platform Awareness:

• Understanding of Azure OpenAI Service Azure AI Studio and Google Cloud Vertex AI for hybrid and multi-cloud AI strategies.
• Awareness of alternative LLM platforms (Anthropic Direct OpenAI Cohere Hugging Face) and model selection criteria.
• Knowledge of edge AI deployment patterns and on-premises LLM hosting options for sensitive use cases.

Key Competencies:

• Strategic Thinking & Problem Solving: Ability to analyse and simplify complex problems articulate trade-offs for informed decision-making and make appropriate recommendations.
• Collaboration & Influence: Excellence in collaboration cross-functional expectation management and influencing skills.
• Business Acumen: Ability to translate business requirements into databases data warehouses and data streams.
• Data Management: Experience in creating procedures to ensure data accuracy and accessibility; analysing planning and defining data architecture frameworks (including security reference data metadata and master data); and creating and implementing data management processes and procedures.
• Stakeholder Engagement: Proven ability to collaborate with other teams within the organization to devise and implement data strategies build models and assess shareholder needs and goals.
• Innovation: Aptitude for researching data acquisition opportunities and developing application programming interfaces (APIs) to retrieve data.
• Communication: Excellent written verbal and meeting facilitation skills.

Qualifications :

Bachelors or Masters degree in informatics computer science or a related AI field.

Additional Information :

Note: Syngenta is an Equal Opportunity Employer and does not discriminate in recruitment hiring training promotion or any other employment practices for reasons of race color religion gender national origin age sexual orientation gender identity marital or veteran status disability or any other legally protected status. 

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Employment Type :

Full-time